How the EU Can Achieve Legally Trustworthy AI: A Response to the European Commission’s Proposal for an Artificial Intelligence Act

Smuha, Nathalie A.; Ahmed-Rengers, Emma; Harkens, Adam; Li, Wenlong; Maclaren, James; Piselli, Riccardo; Yeung, Karen

doi:10.2139/ssrn.3899991

Cited by 67 publications

(36 citation statements)

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“…It remains that such a self-regulation approach can be put at risk in terms of effectiveness. Providing firms with a wide margin of discretion may run counter to the objectives of regulation, as [39,40] show about the proposal for a European regulation on A.I. presented by the Commission in April 2021.…”

Section: Discussionmentioning

confidence: 99%

“…The risk of self-regulation is linked to the combination of vague criteria and the delegation of a wide margin of discretion. Smuha et al [39,40] propose an ex-ante certification of algorithms, which we could extend to a certification procedure for self-evaluation tools.…”

Section: Discussionmentioning

confidence: 99%

“…is accompanied by the race to A.I. regulations, nationally and even globally [39,40]. The regulatory playground can take multiple shapes: new regulation, deregulation, re-regulation, soft-law measures, principle-based or rule-based.…”

Section: Motivations and Process For Self-regulationmentioning

confidence: 99%

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Artificial intelligence and consumer manipulations: from consumer's counter algorithms to firm's self-regulation tools

Marcellis-Warin

Marty

Thelisson³

et al. 2022

AI Ethics

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The growing use of artificial intelligence (A.I.) algorithms in businesses raises regulators' concerns about consumer protection. While pricing and recommendation algorithms have undeniable consumer-friendly effects, they can also be detrimental to them through, for instance, the implementation of dark patterns. These correspond to algorithms aiming to alter consumers' freedom of choice or manipulate their decisions. While the latter is hardly new, A.I. offers significant possibilities for enhancing them, altering consumers' freedom of choice and manipulating their decisions. Consumer protection comes up against several pitfalls. Sanctioning manipulation is even more difficult because the damage may be diffuse and not easy to detect. Symmetrically, both ex-ante regulation and requirements for algorithmic transparency may be insufficient, if not counterproductive. On the one hand, possible solutions can be found in counter-algorithms that consumers can use. On the other hand, in the development of a compliance logic and, more particularly, in tools that allow companies to self-assess the risks induced by their algorithms. Such an approach echoes the one developed in corporate social and environmental responsibility. This contribution shows how self-regulatory and compliance schemes used in these areas can inspire regulatory schemes for addressing the ethical risks of restricting and manipulating consumer choice.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence and consumer manipulations: from consumer's counter algorithms to firm's self-regulation tools

Marcellis-Warin

Marty

Thelisson³

et al. 2022

AI Ethics

View full text Add to dashboard Cite

show abstract

“…Standards are expected to appear in 2025presumably around the same time as the AI Act would come into force. 42 However, self-assessment has been criticised for its unreliability, cloudiness and discretionary nature and thus the strengthening of ex ante obligations has been strongly advocated (Smuha et al 2021). Delegation of rulemaking to ESOs is equally problematic since these bodies are governed by private law.…”

Section: Conformity Assessment As Per 'New Legislative Framework' And...mentioning

confidence: 99%

On the Path to the Future: Mapping the Notion of Transparency in the EU Regulatory Framework for AI

Varošanec

2022

SSRN Journal

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“…However, despite efforts at expanding transparency in this context, the new draft regulation falls significantly short in terms of mitigating informational disbalances. The proposed regulation adopts a risk-based approach -regulating "high-risk" AI systems 4 -and has been criticised for important shortcomings, not least among these, that the regulation of the high-risk systems centres on conformity assessment with a range of essential requirements based largely on a system of internal checks, that is, self-assessment by providers (Veale & Zuiderveen Borgesius 2021;Smuha et al 2021). As a result: "there are 'almost no situations' in which such industry AI self-assessments will require approval by an independent technical organization, and even then, such organizations are usually private sector certification firms accredited by Member States".…”

Section: Regulatory Efforts: the Eu Draft Ai Regulationmentioning

confidence: 99%

AI algorithmic oversight: new frontiers in regulation

2022

Innovation and Regulatory Agencies

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The rise of artificial intelligence (AI) is transforming our societies, permeating an ever-expanding array of domains ranging from finance, to employment or healthcare. AI is prognosticated to be a key driver of economic growth in years to come (European Commission 2018). Importantly, the reliance on the technology is also increasingly spilling over from private into public domains. AI is becoming a "game changer" for public bureaucracies and our public institutions, with public power increasingly exercised algorithmically. Public regulators, among others, increasingly rely on machine learning techniques in the discharge of regulatory tasks (Yeung 2018; Yeung & Lodge 2019) -for instance, in prioritising regulatory targets for regulatory attention and/or enforcement, in areas as varied as environmental protection, securities or food safety. Algorithmic enforcement tools are being relied upon by public regulators, among others, to identify misconduct, to detect fraud in reporting, to prioritise sites or regulatees for inspection, in an effort to optimise the use of limited resources at their disposal (Engstrom et al. 2020;Coglianese & Lehr 2017). But the use of AI in government stretches well beyond the field of regulation, making significant inroads into the coercive and (re)distributive powers of the state itself (Pasquale 2011;Engstrom et al. 2020). Algorithmic outputs are used by police to predict crime in the context of predictive policing technologies; for teacher appraisals and to inform school reforms; and by public authorities to allocate grants, entitlements and benefits (

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How the EU Can Achieve Legally Trustworthy AI: A Response to the European Commission’s Proposal for an Artificial Intelligence Act

Cited by 67 publications

References 0 publications

Artificial intelligence and consumer manipulations: from consumer's counter algorithms to firm's self-regulation tools

Artificial intelligence and consumer manipulations: from consumer's counter algorithms to firm's self-regulation tools

On the Path to the Future: Mapping the Notion of Transparency in the EU Regulatory Framework for AI

AI algorithmic oversight: new frontiers in regulation

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